Bibliographic record
Abstract
In 2009, a major study by the World Health Organization (WHO) in conjunction with the Harvard School of Public Health was published in The New England Journal of Medicine. This study demonstrated significant improvement in surgical complications and mortality with the implementation of a simple checklist (Table) used preoperatively, perioperatively, and postoperatively.1 This prospective observational study was carried out in 8 hospitals around the globe and compared outcomes before checklist implementation (3733 consecutive patients) and after implementation (3955 patients). All patients underwent noncardiac surgery and were at least 16 years old. There was no difference in the proportion of urgent cases, outpatient cases, use of general anesthesia, or case mix. Medical personnel were formally educated on the use of checklists before implementation. Complications were defined by the American College of Surgeons’ National Surgical Quality Improvement Program: acute renal failure; bleeding requiring transfusion of ≥ 4 units of red cells within the first 72 hours after surgery; cardiac arrest requiring cardiopulmonary resuscitation; coma of ≥ 24 hours’ duration; deep venous thrombosis; myocardial infarction; unplanned intubation; ventilator use for ≥ 48 hours; pneumonia; pulmonary embolism; stroke; major disruption of wound; infection of surgical site; sepsis; septic shock; systemic inflammatory response syndrome; unplanned return to the operating room; vascular graft failure; and death. Patients were followed up until discharge or for up to 30 days, whichever came first. When the results of all centers were combined, the rate of complications and death decreased from 11% to 7% (P < .001) and 1.5% to 0.8% (P = .003), respectively. Notably, 2 sites did not use intraoperative pulse oximetry and 3 sites did not use routine preoperative prophylactic antibiotics before checklist implementation. However, exclusion of any 1 site from the statistical analysis did not affect the significance of the outcomes. Despite a number of shortcomings with the study, the marked benefit in overall morbidity and mortality led to the rapid adoption of surgical checklists in the United States after the publication of the study. This is likely due to the low cost and low risk of implementation. Additionally, the findings have been bolstered by several subsequent smaller studies with similar results. But can the results of this WHO study be generalized, especially to hospitals in developed countries?From N Engl J Med, Haynes AB, Weiser TG, Berry WR, et al, A surgical safety checklist to reduce morbidity and mortality in a global population, Volume No. 360(5), Page No. 491-499, Copyright © (2009) Massachusetts Medical Society. Reprinted with permission from Massachusetts Medical Society.Recently, a large study was published in The New England Journal of Medicine examining the effect of surgical checklist implementation in Ontario, Canada.2 Of the 133 hospitals in Ontario, 101 hospitals were included in the analysis, providing data on 109 341 operations before implementation and 106 370 operations after implementation. Seventy-nine centers used the Canadian Patient Safety Institute checklist; 9 used customized checklists; and 4 used the WHO checklist. Nine centers did not provide information on the checklist used. Outcome was independent of which checklist was used. Self-reported checklist compliance was 99% to 100% at 97 hospitals; the lowest reported compliance was 91.6%. In contrast to the WHO study, which included only complications occurring during the postoperative inpatient stay, all complications occurring within 30 days of operation were analyzed. The definition of a complication was the same definition used in the WHO study. The results demonstrated no significant difference in mortality rates (0.71% vs 0.65%) or complication rates (3.86% vs 3.82%). Hospital length of stay showed a slight, significant reduction after checklist implementation (5.11 vs 5.07 days; P = .003). There was no difference in the number of emergency department visits or readmissions. Thus, the results of this Canadian study do not support the robust improvement in surgical safety demonstrated by the WHO. Does this mean that hospitals around North America have misallocated resources in the implementation of surgical checklists? Criticism of the recent Ontario study derives from a potential lack of consistency in proper use of the surgical checklist resulting from a lack of formalized training of hospital staff. In addition, hospitals self-reported the use of this mandatory checklist, and the reported compliance may be inflated compared with the WHO study, which carefully monitored use. Additionally, no single checklist was used by all centers. These factors may confound study results to some extent, but the methodology of the study reflects the effect of checklist implementation in a real-world scenario within a developed nation. Operating on a human being is an extraordinarily complex and orchestrated task that requires attentiveness to detail and situational awareness by all team members. Checking a box is no substitute for critical thinking. Although there is no harm in spending 1 to 2 minutes ensuring that the simplest (and easiest to overlook) components of a successful procedure are in order, reliance on checklists beyond this should be avoided. Common sense and prudence should be exercised in the implementation and adaptation of surgical safety checklists to ensure that hospital staff and administration understand the significance and the limitations of the checklist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".